Running ahead of evolution - AI based simulation for predicting future high-risk SARS-CoV-2 variants

Chen, J.; Nie, Z.; Wang, Y.; Wang, K.; Xu, F.; Hu, Z.; Zheng, B.; Wang, Z.; Song, G.; Zhang, J.; Fu, J.; Huang, X.; Ren, Z.; Wang, Q.; Li, D.; Wei, D.; zhou, B.; Yang, C.; Tian, Y.

doi:10.1101/2022.11.17.516989

bioRxiv · 10.1101/2022.11.17.516989

Running ahead of evolution - AI based simulation for predicting future high-risk SARS-CoV-2 variants

Chen, J.·Nie, Z.·Wang, Y.·Wang, K.·Xu, F.·Hu, Z.·Zheng, B.·Wang, Z.·Song, G.·Zhang, J.·Fu, J.·Huang, X.·Ren, Z.·Wang, Q.·Li, D.·Wei, D.·zhou, B.·Yang, C.·Tian, Y.

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Abstract

The never-ending emergence of SARS-CoV-2 variations of concern (VOCs) has challenged the whole world for pandemic control. In order to develop effective drugs and vaccines, one needs to efficiently simulate SARS-CoV-2 spike receptor binding domain (RBD) mutations and identify high-risk variants. We pretrain a large protein language model with approximately 408 million protein sequences and construct a high-throughput screening for the prediction of binding affinity and antibody escape. As the first work on SARS-CoV-2 RBD mutation simulation, we successfully identify mutations in the RBD regions of 5 VOCs and can screen millions of potential variants in seconds. Our workflow scales to 4096 NPUs with 96.5% scalability and 493.9x speedup in mixed precision computing, while achieving a peak performance of 366.8 PFLOPS (reaching 34.9% theoretical peak) on Pengcheng Cloudbrain-II. Our method paves the way for simulating coronavirus evolution in order to prepare for a future pandemic that will inevitably take place. Our models are released at https://github.com/ZhiweiNiepku/SARS-CoV-2_mutation_simulation to facilitate future related work. JustificationWe develop a novel multi-constraint variation prediction framework to simulate SARS-CoV-2 RBD mutations, reaching a peak performance of 366.8 PFLOPS with 96.5% scalability and achieving 493.9x speedup. Our method facilitates the prediction and prioritization of future high-risk variants for the early deployment of drugs and vaccines. Performance attributes O_TBL View this table: org.highwire.dtl.DTLVardef@1b1873aorg.highwire.dtl.DTLVardef@25314eorg.highwire.dtl.DTLVardef@1c80130org.highwire.dtl.DTLVardef@1c61b21org.highwire.dtl.DTLVardef@11f21c5_HPS_FORMAT_FIGEXP M_TBL C_TBL Overview of the problemCoronavirus Disease 2019 (COVID-19) has spread rapidly to more than 200 countries or regions since December 2019. Due to its high infectivity, there have been over 645 million confirmed cases, including approximately 6.6 million deaths, reported by the World Health Organization (WHO) as of December 20221. In addition to being a serious threat to human health, COVID-19 has had a catastrophic impact on the global economy.

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Chen, J., Nie, Z., Wang, Y., Wang, K., Xu, F., Hu, Z., Zheng, B., Wang, Z., Song, G., Zhang, J., Fu, J., Huang, X., Ren, Z., Wang, Q., Li, D., Wei, D., zhou, B., Yang, C., Tian, Y.. 2022-11-19. Running ahead of evolution - AI based simulation for predicting future high-risk SARS-CoV-2 variants. https://doi.org/10.1101/2022.11.17.516989

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